Reduced-Item Food Audits Based on the Nutrition Environment Measures Surveys


      The community food environment may contribute to obesity by influencing food choice. Store and restaurant audits are increasingly common methods for assessing food environments, but are time consuming and costly. A valid, reliable brief measurement tool is needed. The purpose of this study was to develop and validate reduced-item food environment audit tools for stores and restaurants.


      Nutrition Environment Measures Surveys for stores (NEMS-S) and restaurants (NEMS-R) were completed in 820 stores and 1,795 restaurants in West Virginia, San Diego, and Seattle. Data mining techniques (correlation-based feature selection and linear regression) were used to identify survey items highly correlated to total survey scores and produce reduced-item audit tools that were subsequently validated against full NEMS surveys. Regression coefficients were used as weights that were applied to reduced-item tool items to generate comparable scores to full NEMS surveys. Data were collected and analyzed in 2008–2013.


      The reduced-item tools included eight items for grocery, ten for convenience, seven for variety, and five for other stores; and 16 items for sit-down, 14 for fast casual, 19 for fast food, and 13 for specialty restaurants—10% of the full NEMS-S and 25% of the full NEMS-R. There were no significant differences in median scores for varying types of retail food outlets when compared to the full survey scores. Median in-store audit time was reduced 25%–50%.


      Reduced-item audit tools can reduce the burden and complexity of large-scale or repeated assessments of the retail food environment without compromising measurement quality.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to American Journal of Preventive Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Glanz K.
        • Mullis R.M.
        Environmental interventions to promote healthy eating: a review of models, programs, and evidence.
        Health Educ Q. 1988; 15: 395-415
        • Swinburn B.
        • Egger G.
        • Raza F.
        Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity.
        Prev Med. 1999; 29: 563-633
        • Glanz K.
        • Sallis J.F.
        • Saelens B.E.
        • Frank L.D.
        Healthy nutrition environments: concepts and measures.
        Am J Health Promot. 2005; 19: 330-333
        • Casagrande S.S.
        • Franco M.
        • Gittelsohn J.
        • et al.
        Healthy food availability and the association with BMI in Baltimore, Maryland.
        Public Health Nutr. 2011; 14: 1001-1007
        • Zick C.D.
        • Smith K.R.
        • Fan J.X.
        • Brown B.B.
        • Yamada I.
        • Kowaleski-Jones L.
        Running to the store? The relationship between neighborhood environments and the risk of obesity.
        Soc Sci Med. 2009; 69: 1493-1500
        • Dubowitz T.
        • Ghosh-Dastidar M.B.
        • Collins R.
        • Escarce J.
        Food policy research: we need better measurement, better study designs, and reasonable and measured actions based on the available evidence.
        Obesity. 2013; 21: 5-6
        • Block J.P.
        • Christakis N.A.
        • OʼMalley A.J.
        • Subramanian S.V.
        Proximity to food establishments and body mass index in the Framingham Heart Study offspring cohort over 30 years.
        Am J Epidemiol. 2011; 174: 1108-1114
        • Gustafson A.A.
        • Sharkey J.
        • Samuel-Hodge C.D.
        • et al.
        Perceived and objective measures of the food store environment and the association with weight and diet among low-income women in North Carolina.
        Public Health Nutr. 2011; 14: 1032-1038
        • Lamichhane A.P.
        • Puett R.
        • Porter D.E.
        • Bottai M.
        • Mayer-Davis E.J.
        • Liese A.D.
        Associations of built food environment with body mass index and waist circumference among youth with diabetes.
        Int J Behav Nutr Phys Act. 2012; 9: 81
        • Kelly B.
        • Flood V.M.
        • Yeatman H.
        Measuring local food environments: an overview of available methods and measures.
        Health Place. 2011; 17: 1284-1293
        • McKinnon R.A.
        • Reedy J.
        • Morrissette M.A.
        • Lytle L.A.
        • Yaroch A.L.
        Measures of the food environment: a compilation of the literature, 1990-2007.
        Am J Prev Med. 2009; 36: S124-S133
        • Holsten J.E.
        Obesity and the community food environment: a systematic review.
        Public Health Nutr. 2009; 12: 397-405
        • Caspi C.E.
        • Sorensen G.
        • Subramanian S.V.
        • Kawachi I.
        The local food environment and diet: a systematic review.
        Health Place. 2012; 18: 1172-1187
        • Han E.
        • Powell L.M.
        • Zenk S.N.
        • Rimkus L.
        • Ohri-Vachaspati P.
        • Chaloupka F.J.
        Classification bias in commercial business lists for retail food stores in the U.S.
        Int J Behav Nutr Phys Act. 2012; 9: 46
        • Vernez Moudon A.
        • Drewnowski A.
        • Duncan G.E.
        • Hurvitz P.M.
        • Saelens B.E.
        • Scharnhorst E.
        Characterizing the food environment: pitfalls and future directions.
        Public Health Nutr. 2013; 16: 1238-1243
      1. National Center for Chronic Disease Prevention and Health Promotion, CDC. Census tract level state maps of the modified Retail Food Environment Index (mRFEI). Accessed July 20, 2015.

        • Williams L.K.
        • Thornton L.
        • Crawford D.
        • Ball K.
        Perceived quality and availability of fruit and vegetables are associated with perceptions of fruit and vegetable affordability among socio-economically disadvantaged women.
        Public Health Nutr. 2012; 15: 1262-1267
        • Ball K.
        • Jeffery R.W.
        • Crawford D.A.
        • Roberts R.J.
        • Salmon J.
        • Timperio A.F.
        Mismatch between perceived and objective measures of physical activity environments.
        Prev Med. 2008; 47: 294-298
        • Ohri-Vachaspati P.
        • Leviton L.C.
        Measuring food environments: a guide to available instruments.
        Am J Health Promot. 2010; 24: 410-426
        • Glanz K.
        • Sallis J.
        • Saelens B.
        • Frank L.
        Nutrition Environment Measures Survey in Stores (NEMS-S): development and evaluation.
        Am J Prev Med. 2007; 32: 282-289
        • Saelens B.
        • Glanz K.
        • Sallis J.
        • Frank L.
        Nutrition Environment Measures Study in Restaurants (NEMS-R): development and evaluation.
        Am J Prev Med. 2007; 32: 273-281
        • Honeycutt S.
        • Davis E.
        • Clawson M.
        • Glanz K.
        Training for and dissemination of the Nutrition Environment Measures Surveys (NEMS).
        Prev Chronic Dis. 2010; 7: 6
      2. NEMS Store Measures (NEMS-S). Nutrition Environment Measures Survey. Accessed July 20, 2015.

      3. NEMS Restaurant Measures (NEMS-R). Nutrition Environment Measures Survey. Accessed July 20, 2015.

        • Saelens B.E.
        • Sallis J.F.
        • Frank L.D.
        • et al.
        Obesogenic neighborhood environments, child and parent obesity: the Neighborhood Impact on Kids study.
        Am J Prev Med. 2012; 42: e57-e64
        • Frank L.D.
        • Saelens B.E.
        • Chapman J.
        • et al.
        Objective assessment of obesogenic environments in youth: geographic information system methods and spatial findings from the Neighborhood Impact on Kids study.
        Am J Prev Med. 2012; 42: e47-e55
        • Fayyad U.M.
        • Piatetsky-Shapiro G.
        • Smyth P.
        From data mining to knowledge discovery: an overview.
        in: Fayyad U.M. Piatetsky-Shapiro G. Smyth P. Uthurusamy R. Advances in Knowledge Discovery and Data Mining. AAAi Press/MIT Press, Menlo Park1996: 1-36
        • Hall M.
        • Holmes G.
        Benchmarking attribute selection techniques for discrete class data mining.
        IEEE Trans Knowl Data Eng. 2003; 15: 1437-1447
        • Witten I.A.
        • Frank E.
        • Hall M.A.
        Implementation: real machine learning schemes.
        in: Witten I.A. Frank E. Hall M.A. Data Mining: Practical Machine Learning Tools and Techniques. 3rd ed. Morgan Kaufmann Publishers, Burlington, MA2011: 191-304
      4. U.S. Census Bureau. American Community Survey: 2009-2013 ACS 5-year estimates. Accessed May 28, 2015.